Simulation & Production Design Systems

Globalization of the marketplace has brought diverse customer needs into the arena of production. In order to respond with deliberate speed, organizations have adopted manufacturing strategies that are more flexible and responsive.

At one time batch production was the main means of industrial production. Production quotas were pieced together using past performance records and projected costs. Putting a new product on the market was a combination of past experience and perceived market trends. The ability and experience of executive and management personnel kept companies safe in times of shifting economies. Currently, with computer systems able to correlate information and interpret trends faster than individuals, most of that has changed.

Through the use of computer simulation and graphical animation, a multinational now has the ability to project in facts and figures what it might want in terms of future production (Watson and Wood, 1995, 53). A contemporary production system must be able to support a diverse product mix, respond quickly to a dynamic environment, maintain minimal inventory levels, provide high quality and low cost products. Mixed model production, as it has come to be called, combines the strengths of material requirements planning (MRP), push style production and a kanban (justintime) pull style production. This improves customer service, increases productivity and he

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n production, engineering, and management. Whirlpool needed the means to anticipate startup problems and to fine tune their system prior to the actual scheduled startup (Watson and Wood, 1995, 53). The primary objective of the Whirlpool project was to develop a model that could accurately predict the throughput of the proposed production system. The system parameters that could potentially impact throughput were determined: conveyor speeds, failure rates, product flow, production sequence, selection logic, and so on. This activity of determining what is important and what is not important is what determines the model's level of detail (Watson and Wood, 1995, 55). The basic simulation process begins with formulating the problem and defining the objectives. This is followed by model conceptualization and data collection, model translation and verification, model validation, design of experiments and statistical analysis (Watson and Wood, 1995, 55). Model translation refers to the computer implementation of a particular conceptual model. In Whirlpool's case, the ARENA simulation system was used. Model verification refers to the process of assuring that the computer program of the model and its implementation are correct. T
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Approximate Word count = 3769
Approximate Pages = 15 (250 words per page)